Learning to discriminate new shapes ultimately depends on processing changes at the level of individual neurons and neural populations. We sought to quantify and model the precise neural tuning changes that underlie shape learning. We trained a macaque monkey to discriminate 8 categories of letter-like stimuli. These stimuli were configurations of medial axis components (line segments, curves, junctions) drawn from a common set of 8 components. This enforced learning of global shape, since stimuli sharing components were confusable on a local level. In addition, the 8 stimuli constituted only a subset of the potential component configurations, making it possible to test for processing of specific configurations. After training we characterized neural tuning functions in anterior inferotemporal cortex (AIT), the final stage in the object pathway of monkey visual cortex. We used a genetic algorithm to guide large-scale sampling (500–1500 initially random, progressively evolving stimuli) in the medial axis shape domain. Our previous study showed that this method can constrain quantitative, predictive models of AIT tuning for medial axis structure (Hung et al., Neuron, 2012). Here, we hypothesized that a substantial number of AIT neurons would show structural tuning for medial axis components and component configurations unique to the learned stimuli. This hypothesis was confirmed in our current sample of AIT neurons. Many neurons were tuned for components common to multiple stimuli. Many neurons were tuned for configurations diagnostic for specific stimuli. We did not observe tuning for potential component configurations outside the learned stimulus set. This is the first direct observation of structural tuning changes that could explain visual shape learning.